Skill UI

This architecture provides a scalable, production-ready framework for integrating AI candidate analysis with Juicebox. It manages automated data ingestion from job descriptions, ranks candidates using intelligent scoring, handles result caching, and facilitates seamless data export to major ATS platforms like Greenhouse and Lever. Designed for high-volume, complex hiring pipelines requiring data freshness, deduplication, and robust scaling.

A comprehensive guide applying Kaizen (continuous improvement) and Poka-Yoke (error proofing) principles to software development. Use this skill when you need to enhance code quality, refactor large systems incrementally, validate designs, or optimize development processes by focusing on prevention rather than fixing bugs.

This skill provides a comprehensive pattern for building robust, scalable, and fault-tolerant asynchronous video generation pipelines using Kling AI. It implements advanced software engineering patterns, including Redis queues (Producer/Consumer), State Machines, and event-driven callbacks. It is designed for managing complex, multi-step AI workflows—such as generate, extend, and download—ensuring reliable background task processing.

A comprehensive checklist designed for ensuring that Kling AI video generation integrations are robust, secure, and reliable before going live or during a deployment review. It covers critical areas including authentication protocols, detailed error handling (retry logic, timeouts), cost control mechanisms, content safety policies, and necessary monitoring setup for a stable production environment.

This skill provides comprehensive strategies for managing API rate limits, focusing on high-volume and mission-critical workflows. It implements exponential backoff with jitter, task concurrency limiting using `asyncio`, and request queue patterns. Designed to detect and gracefully handle `429 Too Many Requests` errors, ensuring reliable and stable execution even under severe throttling conditions.

This is a production-grade reference architecture for building scalable video generation platforms powered by Kling AI. It details the complete pipeline, from the API gateway and job queuing (Redis/SQS) to the worker pool, object storage, and CDN delivery. Use this guide when designing high-throughput, resilient, and scalable AI video systems.

This guide details how to implement webhooks for Kling AI video generation tasks. Instead of inefficiently polling the API status, you can provide a callback URL when submitting a task. When the task completes (whether successful or failed), Kling AI will automatically send a POST request containing the full result to your designated endpoint. This mechanism eliminates polling overhead and is essential for building robust, event-driven video processing pipelines.

This guide provides comprehensive patterns and best practices for designing high-impact Key Performance Indicator (KPI) dashboards. It covers everything from establishing a KPI framework (Strategic, Tactical, Operational) to defining departmental metrics (Sales, Marketing, Finance) and implementing best-practice layouts for executive summaries and SaaS metrics. Use this skill to translate raw data into actionable business insights.

An expert guide for designing, implementing, and optimizing large-scale, secure, and highly available cloud-native infrastructure using Kubernetes. Covers advanced topics including GitOps workflows (ArgoCD/Flux), service mesh architecture (Istio), multi-cluster management, Policy as Code (OPA), and infrastructure automation with Terraform. Ideal for platform engineers building enterprise-grade container orchestration solutions.

Guides Kubernetes operators through workload design, manifest generation, security hardening, networking, storage, GitOps, and multi-cluster operations while ensuring probes, resource limits, and least-privilege policies.

A comprehensive CI/CD pipeline designed for LangChain applications. It automates quality assurance through fast, mocked unit tests and slower, more accurate integration tests utilizing real LLMs (e.g., OpenAI). It specifically validates the entire RAG pipeline and integrates tracing with LangSmith, ensuring robust deployment quality.

A comprehensive utility designed to gather all necessary diagnostic evidence for troubleshooting complex LangChain failures. It captures environment snapshots, dependency versions, detailed execution traces (including LLM calls and tool usage), and error stack traces. Use this bundle when preparing detailed GitHub bug reports or conducting LangSmith tracing.